A Distributed Storage Strategy For Trajectory Data Based On Nosql Database

Zhou, Yan; Chen, Qifan; Shan, Baoyu; Fan, Jun; Pang, Yuling

doi:10.1109/igarss.2019.8900482

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…With the development of Internet and GPS navigation and positioning technology, residents generate a huge amount of trajectory data in the process of traveling by car. These data are updated quickly, have huge data volume, and are suitable for distributed storage and retrieval, but there are currently problems such as skewed data storage and slow retrieval speed [1]. At the same time, the trajectory data contains geospatial and temporal information, and is commonly used for comprehensive display by means of heat map and fly line map.…”

Section: Introductionmentioning

confidence: 99%

Trajectory visualization based on multi-level spatio-temporal data organization model

Yao

Zhang

et al. 2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

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Current trajectory data grows rapidly in a dynamic and streaming form, and unreasonable data organization causes the problems of skewed data storage and high overhead, as well as slow retrieval speed and page lag during visualization. To achieve effective spatial data organization, this paper proposes a data storage model with multi-level spatio-temporal organization. Spatially, the trajectory data is partitioned based on Hilbert curve, combined with pre-partitioning mechanism to solve the storage skewing problem of distributed database HBase; temporally, borrowing from the organization of spatio-temporal cube, the spatio-temporal hybrid coding is constructed by using the method of slicing by day and minute system coding to solve the retrieval of trajectory data into maps. The experiment proves that the organization model can effectively improve the data storage and retrieval efficiency, enhance the overall effect of trajectory visualization, and provide effective technical support for data mining and analysis.

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Section: Introductionmentioning

confidence: 99%

Trajectory visualization based on multi-level spatio-temporal data organization model

Yao

Zhang

et al. 2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

View full text Add to dashboard Cite

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A Trajectory Big Data Storage Model Incorporating Partitioning and Spatio-Temporal Multidimensional Hierarchical Organization

Yao

Zhang

et al. 2022

IJGI

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Trajectory big data is suitable for distributed storage retrieval due to its fast update speed and huge data volume, but currently there are problems such as hot data writing, storage skew, high I/O overhead and slow retrieval speed. In order to solve the above problems, this paper proposes a trajectory big data model that incorporates data partitioning and spatio-temporal multi-perspective hierarchical organization. At the spatial level, the model partitions the trajectory data based on the Hilbert curve and combines the pre-partitioning mechanism to solve the problems of hot writing and storage skewing of the distributed database HBase; at the temporal level, the model takes days as the organizational unit, finely encodes them into a minute system and then fuses the data partitioning to build spatio-temporal hybrid encoding to hierarchically organize the trajectory data and solve the problems of efficient storage and retrieval of trajectory data. The experimental results show that the model can effectively improve the storage and retrieval speed of trajectory big data under different orders of magnitude, while ensuring relatively stable writing and query speed, which can provide an efficient data model for trajectory big data mining and analysis.

show abstract

A Distributed Storage Strategy For Trajectory Data Based On Nosql Database

Cited by 2 publications

References 6 publications

Trajectory visualization based on multi-level spatio-temporal data organization model

Trajectory visualization based on multi-level spatio-temporal data organization model

A Trajectory Big Data Storage Model Incorporating Partitioning and Spatio-Temporal Multidimensional Hierarchical Organization

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